Method and apparatus for slope to threshold conversion for process state monitoring and endpoint detection

ABSTRACT

A method for converting a slope based detection task to a threshold based detection task is provided. The method initiates with defining an approximation equation for a set of points corresponding to values of a process being monitored. Then, an expected value at a current point of the process being monitored is predicted. Next, a difference between a measured value at the current point of the process being monitored and the corresponding expected value is calculated. Then, the difference is monitored for successive points to detect a deviation value between the measured value and the expected value. Next, a transition point for the process being monitored is identified based on the detection of the deviation value. A processing system configured to provide real time data for a slope based transition and a computer readable media are also provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 10/318,967, filed on Dec. 13, 2002, and entitled“METHOD AND APPARATUS FOR SLOPE TO THRESHOLD CONVERSION FOR PROCESSSTATE MONITORING AND ENDPOINT DETECTION,” which is herein incorporatedby reference in its entirety for all purposes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to semiconductor manufacturing and moreparticularly to real time metrology for process and wafer statemonitoring in semiconductor manufacturing operations.

2. Description of the Related Art

During semiconductor fabrication, there exists multiple steps where anunderlying substrate is subjected to the formation, modification andremoval of various layers, blanket or patterned. The small featuresizes, tight surface planarity requirements, combined with the constantquest to increase throughput, makes it highly desirable to stop orchange the process parameters when the targeted properties (thickness,resistance, planarity, transparency, chemical composition etc.) of theprocessed film has been achieved, i.e., when an endpoint has beenobtained for the current process step. Of course, some semiconductorfabrication steps transition to a subsequent fabrication step after thecurrent process step has accomplished the task of obtaining the definedwafer characteristics.

Real time metrology for the control of wafer characteristics is now anecessity so that an endpoint or transition point for a particularprocessing operation may be determined. The real time in-situ monitoringof parameters associated with a semiconductor operation providesvaluable information as to an endpoint or a transition point of aprocessing operation. Typically the properties of an object, either thewafer itself or another object, which is strongly linked to the wafer inthe process, undergo monotonic change prior to and after the transition,experiencing an abrupt property variation during the transition itself.This leads to a step-like property for monitoring signal variation incases when the monitored system is small enough and when the transitionoccurs simultaneously for the watch point of the inspection space. Forlarger systems, such as semiconductor wafers, there is a timedistribution, which transfers a step-like transition point into a slopechange, associated with data points corresponding to the processparameter changes, thereby creating an indicator of the endpoint ortransition point. However, slope change detection requires usage ofderivatives, which are associated with reduced signal-to-noise ratiocomplicating this approach.

FIG. 1 is a graph of a thickness of a semiconductor wafer beingmonitored over time during a processing operation, such as aplanarization processing operation. Line 100 represents valuesassociated with an infrared (IR) based sensor for determining anendpoint/transition point during a processing operation. Lines 102represent values associated with a plurality of eddy current sensors(ECS) for capturing thickness of a semiconductor wafer over time duringthe processing. As can be seen, region 104 represents the time where theendpoint/transition point occurs. In region 104, the general slopeassociated with lines 102 and line 100 transition. However, the signalbeing monitored for either the IR monitoring or eddy current monitoringis superimposed with significant and variable background noise thatchanges from run to run. Accordingly, when measuring the derivativebased slope to determine the endpoint, the signal to noise level affectsthe stability and reliability of the endpoint determination. Thus,in-line determination of a transition point predicated upon a slopebased analysis does not provide the robust data necessary forsemiconductor operations.

As a result, there is a need to solve the problems of the prior art toprovide a method and apparatus for providing stable and reliabletransition point determination for processes where the transition pointoccurs through a slope based transition of a monitored processparameter.

SUMMARY OF THE INVENTION

Broadly speaking, the present invention fills these needs by eliminatingthe need to analyze derivative based data and enabling determination ofthe transition point through a threshold analysis in order to improvethe reliability and stability of the determination of a transitionpoint. It should be appreciated that the present invention can beimplemented in numerous ways, including as a method, a system, computerreadable media or a device. Several inventive embodiments of the presentinvention are described below.

In one embodiment, a method for converting a slope based detection taskto a threshold based detection task is provided. The method initiateswith defining an approximation equation for a set of pointscorresponding to values of a process being monitored. Then, an expectedvalue at a current point of the process being monitored is predicted.Next, a difference between a measured value at the current point of theprocess being monitored and the corresponding expected value iscalculated. Then, the difference is monitored for successive points todetect a deviation value between the measured value and the expectedvalue. Next, a transition point for the process being monitored isidentified based on the detection of the deviation value.

In another embodiment, a method for detecting a transition point of aslope based change through a threshold detection is provided. The methodinitiates with monitoring a parameter associated with a transitionpoint. Then, a predicted value of the parameter is calculated from pastvalues of the monitored process parameter. The predicted valuecorresponds to a current value of the process parameter being monitored.Next, a threshold value is defined. Then, a difference between thecurrent value and the predicted value is tracked. Next, when thedifference exceeds the threshold value a transition point is identified.

In yet another embodiment, a semiconductor processing system capable ofdetecting a transition point for slope change transitions through athreshold determination in real time is provided. The system includes aprocessing module configured to process a semiconductor wafer until adefined parameter associated with the semiconductor wafer beingprocessed is obtained. The processing system includes a sensorconfigured to monitor a process parameter associated with a processoperation. The system includes a detector in communication with thesensor. The detector is configured to compare measured values topredicted values, where the measured values indicate a transition pointthrough a slope change. The predicted values are derived from previouslymeasured values. The detector is further configured to track adifference between the measured values and the corresponding predictedvalues to enhance a change at the transition point to enable a thresholddeviation value to be defined. The threshold deviation value indicatesthe transition point associated with the process operation.

In still yet another embodiment, a computer readable media havingprogram instructions for converting a slope based detection task to athreshold based detection task is provided. The computer readable mediaincludes program instructions for defining an approximation equation fora set of points corresponding to values of a process being monitored andprogram instructions for predicting an expected value at a current pointof the process being monitored. Program instructions for calculating adifference between a measured value at the current point of the processbeing monitored and the corresponding expected value are provided.Program instructions for monitoring the difference to detect a deviationvalue between the measured value and the expected value and programinstructions for identifying a transition point for the process beingmonitored based on the detection of the deviation value are included.

Other aspects and advantages of the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, illustrating by way of example the principles ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be readily understood by the followingdetailed description in conjunction with the accompanying drawings, andlike reference numerals designate like structural elements.

FIG. 1 is a graph of a thickness of a semiconductor wafer beingmonitored over time during a processing operation, such as aplanarization processing operation.

FIG. 2 is a graph of an infrared (IR) based trace where the noise levelin the transition region is smoothed to show a transition point inaccordance with one embodiment of the invention.

FIG. 3 is an exemplary graph of a signal trace over time where theperiod of time includes a transition point.

FIG. 4 is an exemplary graph of the signal of FIG. 3 which has beensmoothed to eliminate noise.

FIG. 5 is an exemplary graph of a predicted signal generated from thepoints of the actual signal of FIG. 3 in accordance with one embodimentof the invention.

FIG. 6 is an exemplary graph of a deviation signal generated bycalculating the difference between an actual signal and a correspondingpredicted signal at various time points in accordance with oneembodiment of the invention.

FIG. 7 is an exemplary graph including the graphs of FIGS. 4, 5 and 6superimposed over each other to illustrate a threshold determination forthe transition point in accordance with one embodiment of the invention.

FIG. 8A is an exemplary graph illustrating a trace of an actual signaland a predicted signal in accordance with one embodiment of theinvention.

FIG. 8B is a graph of the original signal value and a correspondinggraph of a deviation between the actual signal and a predicted signal inaccordance with one embodiment of the invention.

FIG. 8C is a graph illustrating the actual trace and the delta trace ofFIGS. 8A and 8B superimposed with the predicted value trace, all ofwhich are used for the conversion of a slope based task to a thresholdbased task in accordance with one embodiment of the invention.

FIG. 9 is a flowchart of the method operations for a method forconverting a slope based detection task to a threshold based detectiontask in accordance with one embodiment of the invention.

FIG. 10 is a high-level schematic diagram of a processing module incommunication with a detector configured to convert a slope detectiontransition point to a threshold detection transition point in accordancewith one embodiment of the invention.

FIG. 11 is a simplified schematic diagram of a CMP system having aninfrared sensor to determine a transition point of a semiconductorsubstrate being processed in accordance with one embodiment of theinvention.

FIG. 12 is a simplified schematic diagram of a processing system havingeddy current sensors configured to determine a transition point of asemiconductor substrate being processed in accordance with oneembodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An invention is described for a system, device and method that providesa reliable and stable measure of a transition point during asemiconductor processing operation. It will be obvious, however, to oneskilled in the art, that the present invention may be practiced withoutsome or all of these specific details. In other instances, well knownprocess operations have not been described in detail in order not tounnecessarily obscure the present invention. FIG. 1 is described in the“Background of the Invention” section. The term about as used to hereinrefers to +/−10% of the referenced value.

The embodiments of the present invention provide a system and method forconverting a slope detection task to a threshold detection task, as wellas amplifying the transition features that are indicative of atransition. Threshold behavior, produces a step-like feature on thebackground of a zero or a constant signal and provides simpler detectionalgorithms, higher signal-to-noise ratios and more robust detectability.Threshold based determination for transition points/endpoints providesincreased stability and reliability as compared to slope baseddeterminations. In turn, more stable and reliable triggering conditionsare provided with respect to a semiconductor operation. That is, sincethe transition point/endpoint is determined with a high degree ofconfidence, the downstream operations requiring substantially precisemeasurements may be initiated through in-line metrology. As a result,semiconductor throughput for the associated operations may beconcomitantly increased. The complications resulting from small changesin slopes of the process tracing signal with respect to derivativeanalysis are eliminated by the conversion to a threshold type of tracingsignal. It should be appreciated that the terms endpoint and transitionpoint, as used herein, are interchangeable. It will be apparent to oneskilled in the art that the terms endpoint and transition point mayrefer to any point in a semiconductor process, or any other process,where a targeted change is occurring, a targeted state is achieved, orwhere the attainment of a certain value associated with a monitoredparameter is used to trigger an event, such as an endpoint or initiationof another process operation.

FIG. 2 is a graph of an infrared (IR) based trace where the noise levelin the transition region is smoothed to show a transition point inaccordance with one embodiment of the invention. Here, in region 106,the noise level is substantially eliminated to show trace 108 as asmoothed line. As can be seen, the slope associated with smoothed trace108 transitions in region 106. However, the noise associated with theoperation being monitored, e.g., a chemical mechanical planarization(CMP) operation or an etch operation, prevents real time stable andreproducible readings to trigger an event, such as an endpoint orinitiation of another operation. It should be appreciated that whileFIG. 2 illustrates an infrared signal related to temperature, othersignals, e.g. eddy current sensors, vibration, optical refraction, etc.,will exhibit similar properties. That is, the smoothed slope willindicate a transition point. However, as stated above, the backgroundnoise prevents a stable an accurate real time reading from the slopereading. As will be explained in more detail below, the conversion ofthe slope measurement to a threshold measurement removes theuncertainties associated with the noise levels that impact the smalldifferences being analyzed for the derivative measurements associatedwith a slope measurement. Thus, a for more precise detection of atransition point in real time is enabled. The small changes in slopethat indicate a transition point are masked by the noise in a slopemeasurement, however, the embodiments described herein substantiallyeliminate the noise from impacting the determination of the transitionpoint.

FIG. 3 is an exemplary graph of a signal trace over time, where theperiod of time includes a transition point. Here, signal 110 is plottedover time. In one embodiment, signal 110 originates from an eddy currentsensor configured to detect a process parameter indicating a transitionpoint during a semiconductor operation. It should be appreciated thatother signals for detecting transition points during semiconductoroperations can be included here, such as infrared, vibration,optical-based signals, etc. As can be seen, signal 110 has a significantamount of noise associated with the signal, however, a trend over timeindicates a transition point at time 100. One skilled in the art willappreciate that a slope based determination would yield unstable resultshere, due to the background noise.

FIG. 4 is an exemplary graph of the signal of FIG. 3 which has beensmoothed to eliminate noise. Here, line 110 a has had a regularsinusoidal noise filter applied in order to smooth the signal. Wheresignal 110 a represents the values of an eddy current sensor over time,a transition point is shown by a change in slope at approximately time100. As mentioned above, the smoothed slope is not able to be producedin a stable and precise manner in real time. Therefore, a slope baseddetermination is not a viable alternative here.

FIG. 5 is an exemplary graph of a predicted signal generated from thepoints of the actual signal of FIG. 3 in accordance with one embodimentof the invention. Here, an approximation equation for a selectedinterval of data flow points is used to predict the most recent datareading in one embodiment of the invention. As will be explained in moredetail with reference to FIGS. 8A and 8B, the predicted signal isgenerated by applying the approximation equation to historical datapoints of the actual signal to generate a predicted real-time data pointin accordance with one embodiment of the invention. As shown in FIG. 5,the predicted signal represented by line 110 b begins to deviate fromthe actual signal of FIGS. 3 and 4 at time 100, which represents atransition point. It should be appreciated that the approximationequation may be any polynomial equation. For example, the approximationequation may take the form of a linear equation, a parabolic equation orany other higher power equation.

FIG. 6 is an exemplary graph of a deviation signal generated bycalculating the difference between an actual signal and a correspondingpredicted signal at various time points in accordance with oneembodiment of the invention. Here, line 112 represents the differencebetween the predicted signal and the actual signal over time. Region 114represents a corridor of noise. That is, an offset can be applied to thedifference between the actual signal and the predicted signal to definea corridor of noise in which points outside of the corridor of noisewould be considered valid points for a transition point. In essence, thecorridor of noise defines a boundary around the difference between theactual value and the predicted value. In one embodiment, the offset ischosen as 3 times the value of the standard deviation, i.e., 3σ. Thus,where the noise distribution is configured as a Gaussian distribution,then 3σ will cover 99.7% of all the points of the distribution.Therefore, out of 1000 points, a probability of 3×10⁻³ exists for onepoint to be actually outside of the corridor of noise and to not be thestart of a transition point. Depending on the nature of the processbeing monitored, this probability can be reduced or increased to providean acceptable level of accuracy.

FIG. 7 is an exemplary graph including the graphs of FIGS. 4, 5 and 6superimposed over each other to illustrate a threshold determination forthe transition point in accordance with one embodiment of the invention.Here, line 110 a from FIG. 4 line 110 b from FIG. 5 and line 112 aresuperimposed over each other. In addition, the corridor of noise 114 isalso illustrated. It should be appreciated that line 112, whichrepresents the deviation signal, i.e., the difference between the actualsignal and the predicted signal, begins to exceed the corridor of noiseat the transition point of time 100. As will be explained in furtherdetail below, the 3σ corridor (of noise) can be adjusted in order togive a higher or lower confidence level that a transition point is beingdetected. For example, processing requiring high degrees of accuracy andprecision may use a wider corridor of noise than processes requiring aless degree of accuracy and precision. In another embodiment, more thanone consecutive point outside the boundaries of the corridor of noisemay be required to trigger acknowledging a transition point.

FIG. 8A is an exemplary graph illustrating a trace of an actual signaland a predicted signal in accordance with one embodiment of theinvention. Line 120 represents a plot of a process parameter readingover time. It should be appreciated that the process parameter a processparameter associated with a film thickness in one embodiment. Region 121of the graph represents the pre-transition region, while region 123 ofthe graph represents the post transition region. Thus, line 120represents two continuous processes, e.g., a pre-transition regionprocess and a post-transition region process, and in between the twoprocesses is an interrupt in the form of a transition point. Thetransition point here indicates a change from one state to anotherstate. While the embodiments described herein refer to semiconductorprocesses, it should be appreciated that the methodology may be appliedto any system where a transition is occurring and a slope detectionprocess may capture the transition from one state to another state.

Still referring to FIG. 8A, a previous section or interval of data, forexample section n of region 121, is used to predict the value at a latertime point. In one embodiment, a polynomial equation, such as aparabolic equation is used to derive the predicted value correspondingto a most recent data reading. Here, a section of data, also referred toas a data interval, is taken and a data value projected forward by adistance m is calculated. One skilled in the art will appreciate that inone embodiment the parabolic (or higher power) approximation used toproject the data value is of the form:

-   -   Y=a₁t^(n)+a₂t^(n−1)+ . . . +a_(n) where Y represents the        predicted value (y coordinate value) and t represents the time        (x coordinate value).        Of course, it is assumed here that the previous data represents        the dynamics of the respective region the data is associated        with.

Thus, the actual signal measured by a detecting device, i.e., sensor,may be compared with the predicted signal which is calculated from anapproximation equation. The actual signal and the predicted signal aresubstantially equal as long as no transition occurs and the projection(extrapolation) distance is not too large. That is, the differencebetween the value of the actual signal and the value of the predictedsignal (represented as Si—Y where Si is the actual signal and Y is thepredicted signal) is close to 0 within the noise level. However, oncethe transition occurs the difference between the actual signal and thepredicted value starts to be increasingly greater than 0, Si—Y>0, asrepresented by Δ 122 of FIG. 8A. Accordingly, a threshold value may nowbe used to detect where the actual signal and the predicted valuedeviate. As will be explained further, the conversion of the slopedetection process to a threshold detection process enhances (amplifies)the transition related changes so that the transition can be moreaccurately and precisely identified.

FIG. 8B is a graph of the original signal value and a correspondinggraph of a deviation between the actual signal and a predicted signal inaccordance with one embodiment of the invention. The upper graph of FIG.8B represents the actual signal of FIG. 8A. For example, with respect tosemiconductor process monitoring, line 120 may represent a traceassociated with a signal from an infrared sensor, an eddy currentsensor, a vibration sensor, a temperature sensor, a sensor configured todetect reflected spectra, etc. A transition occurring at time t_(t) isdetected through deviation trace 128, also referred to as a delta trace.Regions 124 and 126 represent regions where the predicted value deviatesfrom the actual signal as positive and negative differences,respectively, from the actual signal.

Delta trace 128 of FIG. 8B represents the difference between the actualsignal and the predicted signal over time. Region 114 represents acorridor of noise. It should be appreciated that delta trace 128 and thetrace 120 of the actual signal are likely to have background noiseassociated with each trace. Accordingly, trace 120 is likely to besimilar to the trace of FIG. 3 and delta trace 128 is likely to besimilar to the trace of FIG. 6. That is, the traces are likely to havenoise associated with each trace rather than be smooth lines. Therefore,the corridor of noise is configured to take into account the noise levelso that the noise level does not impact the determination of thetransition point. At the same time, the magnitude, also referred to asthe offset of the corridor of noise is chosen so that the transitionoccurs above the noise level, i.e., the corridor of noise is not solarge as to mask the transition point.

In one embodiment, the offset of the corridor of noise is set at 3standard deviations (3σ) of delta trace 128. Thus, where the noise is aGaussian distribution, then 3σ will cover 99.7% of all the points of thedistribution. As described above, a probability of 3×10⁻³ exists for apoint to fall outside of the corridor of noise and not be the start of atransition point, meaning that 3 out of each 1000 points could beactually outside of the defined 3σ interval. As will be explainedfurther, with reference to FIG. 9, a number of consecutive pointsoutside of the corridor of noise may be required to indicate the startof a transition point to further reduce the probability of a falsepositive. In one embodiment, the offset may not be necessary if areasonably high number of consecutive points outside the corridor ofnoise is used or larger than 3σ noise corridor is used. It should beappreciated that any number of consecutive points outside the corridorof noise may be defined as the trigger for a transition point. Thehigher the number of consecutive points required to indicate that atransition point has been reached, the more confidence that the actualtransition point has been obtained. Thus, for precise operations whereit a false positive would be costly and cause irrecoverable damage,three or more consecutive points outside the corridor of noise may bechosen to provide a high degree of confidence. Where the operation isnot as demanding it may desirable to choose less than three points.

FIG. 8C is a graph illustrating the actual trace and the delta trace ofFIGS. 8A and 8B superimposed with the predicted value trace, all ofwhich are used for the conversion of a slope based task to a thresholdbased task in accordance with one embodiment of the invention. Trace 120represents the actual sensor reading. Trace 136 represents theparabolically extrapolated predicted value. Trace 128 represents thedelta trace which is the difference between the actual sensor readingand the corresponding predicted value. The parabolically extrapolatedpredicted value includes a parabolic approximation associated with aselected interval of data points and a parabolic forecast to a mostrecent data reading. Delta trace 128 is calculated by subtracting theparabolically extrapolated predicted value for the current point and anumber of foregoing consecutive points. Thus, a threshold may be definedwhen the delta trace is stably above the noise level. For example,defining a number of consecutive points outside the noise level prior toacknowledging that a transition point has occurred will provide a highconfidence level that the transition point determination is accurate.One skilled in the art will appreciate that a transition point includesan endpoint as used herein. While a parabolic equation is used as theapproximation equation in this embodiment, it should be appreciated thatany polynomial equation may be used as the approximation equation as theshape of the curve defined by the actual sensor reading will impact thetype of approximation equation.

Referring to FIG. 3 and FIG. 6, it can be seen that thresholddetermination of the delta trace approach of FIG. 6 is superior than theslope based approach of FIG. 3. That is, the noise in the slope baseddetermination hides small changes in slope while the noise in thethreshold based determination is eliminated as a concern by defining anoffset or by requiring a number of consecutive points outside thecorridor of noise prior to initiating the recognition of a transitionpoint. Once a transition point has been achieved, it should beappreciated that another defined operation may initiate or an endpointfor a current process may occur. With respect to semiconductormanufacturing, e.g., etch, deposition, CMP operations or any othersurface modification process, a first layer may have been added,removed, planarized or modified, and then the semiconductor wafer may betransitioned to another module in the processing tool or a differentprocessing tool.

FIG. 9 is a flowchart of the method operations for a method forconverting a slope based detection task to a threshold based detectiontask in accordance with one embodiment of the invention. The methodinitiates with operation 140 where an approximation equation is definedfor a set of points. The approximation equation for a set of pointscorresponds to values of a process being monitored. For example, apolynomial equation, such as a parabolic equation, as discussed above,may be the approximation equation. The process being monitored may bemonitored through eddy current sensors, infrared sensors, temperaturesensors, vibration sensors, etc. The method then advances to operation142 where an expected value at a current point of the process beingmonitored is predicted. Here, the polynomial equation takes a datainterval of a number of consecutive points and predicts an expectedvalue for the current point. In essence, the polynomial equation allowsfor forecasting the current point from past data points as describedwith reference to FIG. 8A.

The method of FIG. 9, then proceeds to operation 144 where a differencebetween the measured (actual) value at the current point of the processand the corresponding expected value is calculated. Here, the differencebetween the measured value and the expected (predicted) value will beapproximately 0 until a transition point. It should be appreciated thatthe difference can be measured in terms of subtraction between twovalues, but is not limited to this interpretation of difference. Thedifference may be captured as a delta trace as described above withreference to FIGS. 8A-C. Thus, the difference between the measured valueat the current point of the process and the corresponding expected valueis monitored to detect a deviation value in operation 146. The deviationvalue may be defined as a number of consecutive points outside acorridor of noise, where the number of consecutive points may be one ormore. In other embodiments, the difference may also be calculated as aratio, a square of differences, etc. That is, the term difference asused herein refers to a broad range of measures that indicate ageneralized difference between two values. Alternatively, an offset maybe used, where the offset is high enough to avoid background noise fromindicating a transition point and low enough so that the deviation valueindicating the transition point may be identified, i.e., is not maskedby the offset. The method then advances to operation 148 where atransition point for the process being monitored is identified based onthe detection of the deviation value. Of course, the transition pointmay end the current process operation and trigger another processoperation or simply end the current process.

FIG. 10 is a high-level schematic diagram of a processing module incommunication with a detector configured to convert a slope detectiontransition point to a threshold detection transition point in accordancewith one embodiment of the invention. Processing module 160 may be anyprocessing module, e.g., any associated semiconductor processing modulesuch as a CMP module, etch module, etc. Detector 162 is in communicationwith processing module 160. For example, sensors located withinprocessing module 160 may be used to transmit signals to detector 162.Detector 162 is configured to convert a process in which slopedetermines a transition point to a threshold detection process where athreshold value is used to detect a transition point. That is, theembodiments described with reference to FIGS. 8A, 8B, 8C and 9 convertthe slope detection process into a threshold detection process areperformed by detector 162. Furthermore, the changes occurring in thetransition point are enhanced so that the threshold value can be readilydetected. In one embodiment, the threshold value is defined as adifference between an actual signal and a predicted signal as describedherein. It should be appreciated that detector 162 may be a generalpurpose computer controlling the processing operation being performed inprocessing module 160. Detector 162 may receive signals from sensorsconfigured to monitor a composition change or a state change asdescribed below. Of course, the communication between processing module160 and detector 162 may be through a closed loop.

FIG. 11 is a simplified schematic diagram of a CMP system having aninfrared sensor to determine a transition point of a semiconductorsubstrate being processed in accordance with one embodiment of theinvention. Controller 164 includes detector 162. Controller 164 is incommunication with infrared sensor 166. Infrared sensor is containedwithin carrier plate 168. Semiconductor substrate 172 is supportedagainst carrier film 170 within carrier plate 168. Infrared sensor 166has a line of view through window 178 to semiconductor substrate 172.Polishing pad 174 planarizes a surface of semiconductor substrate 172.Polishing pad 174 is disposed over stainless steel belt 176. Detector162 converts the slope based signal process associated with the infraredsensor to a threshold based signal. In one embodiment, the slope basedsignal is converted to a threshold based operation by defining a deltatrace between an actual signal and a predicted signal as described withreference to FIGS. 8A-C and 9.

FIG. 12 is a simplified schematic diagram of a processing system havingeddy current sensors configured to determine a transition point of asemiconductor substrate being processed in accordance with oneembodiment of the invention. Controller 164 includes detector 162.Controller 164 is in communication with eddy current sensors 180 a and180 b. Eddy current sensors 180 a and 180 b may be configured to monitora process parameter such as thickness of a film disposed oversemiconductor substrate 162. Where the processing system is a CMPprocessing system, polishing pad 174 planarizes a surface ofsemiconductor substrate 172. Polishing pad 174 is disposed over backing176. Detector 162 converts the slope based detection output of eddycurrent sensors 180 a and 180 b to a threshold detection process. Hereagain, detector 162 tracks a difference between measured values andpredicted values in order to detect a threshold deviation value thatindicates a transition point.

FIGS. 11 and 12 depict specific embodiments of sensors used forsemiconductor fabrication. It should be appreciated that the embodimentsdescribed herein may be associated with any type of sensor that iscapable of monitoring a composition change or a state change. Exemplarycomposition changes include surface composition changes where a firstlayer is removed and a second layer is exposed or a surface of anobject, such as a semiconductor wafer, is modified. Exemplary statechanges include the change from a liquid to a gas, liquid to a solid,etc. In fact, a transition point may be defined as a point at which acomposition or a state change occurs. At the transition point, aproperty, which is monitored through the sensors described herein,changes due to the processing conditions. Targeted properties includeproperties such as thickness, resistance, planarity, transparency,vibration, chemical composition, etc. This change is monitored and thetransition point is determined by a threshold detection scheme asdescribed herein. One skilled in the art will appreciate that theembodiments described herein are not limited to the specific types ofsensors described above. More particularly, any sensor configured todetect a signal indicating a composition or a state change may be usedwith the embodiments described above, whether or not the sensors areassociated with a semiconductor fabrication process. It should befurther appreciated that the sensors can detect the change through theobject being processed, e.g., semiconductor wafer, or a component of theprocessing module, e.g., polishing pad. Thus, the transition point maybe detected directly or indirectly. Some exemplary sensors includesensors configured to detect resistance, capacitance, reflected light,vibration, etc.

In summary, the above described invention describes a method and asystem for converting a slope based detection task to a threshold baseddetection task. An approximation equation, such as a polynomialequation, is used to predict an expected value at the current datapoint. A difference between the expected value and the correspondingactual value supplied from a sensor is monitored to detect a transitionpoint. The monitored difference may be captured as a delta trace asdescribed herein. The tracking of the difference allows a thresholdvalue to be defined. In one embodiment, the transition point occurs whena pre-defined number of consecutive points of the monitored differenceare detected outside of a corridor of noise. An offset may also beprovided to define a level where a detected point, i.e., value of adifference between an actual value and a predicted value, outside of theoffset boundary indicates a transition point. By converting the slopedetection task to a threshold detection task, a stable and reliabletransition detection system is provided that avoids the need to analyzederivatives as required by the slope detection tasks. Additionally, thedelta trace described above enhances the changes occurring at thetransition point in order to more readily identify the transition point.

With the above embodiments in mind, it should be understood that theinvention may employ various computer-implemented operations involvingdata stored in computer systems. These operations include operationsrequiring physical manipulation of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. Further, the manipulationsperformed are often referred to in terms, such as producing,identifying, determining, or comparing.

The above described invention may be practiced with other computersystem configurations including hand-held devices, microprocessorsystems, microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers and the like. The invention may alsobe practiced in distributing computing environments where tasks areperformed by remote processing devices that are linked through acommunications network.

The invention can also be embodied as computer readable code on acomputer readable medium. The computer readable medium is any datastorage device that can store data which can be thereafter read by acomputer system. Examples of the computer readable medium include harddrives, network attached storage (NAS), read-only memory, random-accessmemory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical andnon-optical data storage devices. The computer readable medium can alsobe distributed over a network coupled computer system so that thecomputer readable code is stored and executed in a distributed fashion.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Accordingly, the present embodiments are to beconsidered as illustrative and not restrictive, and the invention is notto be limited to the details given herein, but may be modified withinthe scope and equivalents of the appended claims. In the claims,elements and/or steps do not imply any particular order of operation,unless explicitly stated in the claims.

1. A method for converting a slope based detection task to a thresholdbased detection task, comprising: defining a slope based approximationequation for a set of points corresponding to values of a process beingmonitored; monitoring a difference between a measured value at a currentpoint of the process being monitored and a corresponding expected valueto detect a threshold deviation value between the measured value and theexpected value; and identifying an endpoint for the process beingmonitored based on the detection of the threshold deviation value. 2.The method of claim 1, wherein the approximation equation is apolynomial equation.
 3. The method of claim 1, wherein the methodoperation of monitoring a difference between a measured value at acurrent point of the process being monitored and a correspondingexpected value to detect a threshold deviation value between themeasured value and the expected value includes, predicting an expectedvalue at the current point of the process being monitored through theslope based approximation equation.
 4. The method of claim 1, whereinthe measured value is generated by a sensor configured to detect aproperty associated with process being monitored.
 5. The method of claim3 further comprising: tracing the difference between the measured valueand the corresponding expected value; and defining a corridor of noise,the corridor of noise providing a boundary, wherein which the deviationvalue falls outside of the boundary.
 6. The method of claim 1, furthercomprising: identifying an endpoint for the process being monitoredbased on the detection of the threshold deviation value; and identifyingmore than one consecutive points in which the corresponding thresholddeviation value is outside a corridor of noise.
 7. The method of claim1, wherein the approximation equations derives the expected value froman interval of past data generated by a sensor associated with theprocess being monitored.
 8. The method of claim 6, further comprising:in response to identifying the endpoint, triggering an end of theprocessing being monitored.
 9. A method for detecting a transition pointof a slope based change through a threshold detection, comprisingcalculating a predicted value of a monitored parameter from past valuesof the monitored parameter through a slope based approximation, thepredicted value corresponding to a current value of the parameter beingmonitored; tracking a difference between the current value and thepredicted value for successive time points; and identifying a transitionpoint when the difference exceeds a threshold value.
 10. The method ofclaim 9, wherein the threshold value is outside an offset, the offsetconfigured to substantially eliminate background noise from interferingwith identification of the transition point.
 11. The method of claim 9,wherein the method operation of identifying a transition point when thedifference exceeds a threshold value includes, identifying at least twopoints when the difference exceeds the threshold value.
 12. The methodof claim 9 further comprising: tracing the difference between thecurrent value and the corresponding predicted value; and defining acorridor of noise, the corridor of noise providing a boundary, whereinwhich the threshold value falls outside of the boundary.
 13. The methodof claim 9, wherein the parameter is capable of being monitored by asensor configured to detect a composition change or a state changeassociated with an object being processed.
 14. A semiconductorprocessing system capable of detecting a transition point for slopechange transitions through a threshold determination in real time,comprising: a sensor configured to monitor a process parameterassociated with a process operation; and a detector in communicationwith the sensor, the detector configured to compare measured values topredicted values, the measured values indicating a transition pointthrough a slope change, the predicted values being derived frompreviously measured values, the detector further configured to track adifference between the measured values and the corresponding predictedvalues to enhance a change at the transition point.
 15. The system ofclaim 14, wherein enhancing the change at the transition point enables athreshold deviation value to be defined, the threshold deviation valueindicative of the transition point.
 16. The system of claim 15, whereinthe detector is configured to initiate a transition associated with theprocessing system when a difference between the measured values and thepredicted values for at least two consecutive time points exceeds thethreshold deviation value.
 17. The system of claim 14, wherein thesensor is sensitive to variation of the process parameter, the processparameter varying as one of a composition and a state changes, thecomposition and the state changes associated with one of a semiconductorwafer and a component of the processing module.
 18. The system of claim14, wherein the sensor is selected from the group consisting of an eddycurrent sensor, an infrared sensor, a vibration sensor and a sensorconfigured to detect reflected spectra.
 19. The system of claim 14,wherein the detector is configured to associate an offset forsubstantially eliminating background noise interference.
 20. The systemof claim 15, wherein the threshold deviation value occurs at a timepoint corresponding to a slope transition associated with a trace of themeasured values.
 21. A computer readable media having programinstructions for converting a slope based detection task to a thresholdbased detection task, comprising: program instructions for defining aslope based approximation equation for a set of points corresponding tovalues of a process being monitored; program instructions for monitoringa difference between a measured value at a current point of the processbeing monitored and a corresponding expected value to detect a thresholddeviation value between the measured value and the expected value; andprogram instructions for identifying an endpoint for the process beingmonitored based on the detection of the threshold deviation value. 22.The computer readable media of claim 21, wherein the programinstructions for monitoring a difference between a measured value at acurrent point of the process being monitored and a correspondingexpected value to detect a threshold deviation value between themeasured value and the expected value includes, program instructions forpredicting an expected value at the current point of the process beingmonitored through the slope based approximation equation.
 23. Thecomputer readable media of claim 21, further comprising: programinstructions for identifying an endpoint for the process being monitoredbased on the detection of the threshold deviation value; and programinstructions for identifying more than one consecutive points in whichthe corresponding threshold deviation value is outside a corridor ofnoise.